Conservation prioritization in widespread species: the use of genetic and morphological data to assess population distinctiveness in rainbow trout (<i>Oncorhynchus mykiss</i>) from British Columbia, Canada
Bibliographic record
Abstract
Prioritization of efforts to maintain biodiversity is an important component of conservation, but is more often applied to ecosystems or species than within species. We assessed distinctiveness among 27 populations of rainbow trout (Salmonidae: Oncorhynchus mykiss) from British Columbia, Canada, using microsatellite DNA variation (representing historical or contemporary demography) and morphology (representing adaptive variation). Standardized genetic scores, that is, the average deviation across individuals within populations from the overall genetic score generated by factorial correspondence analysis, ranged from 1.05 to 4.90 among populations. Similar standardized morphological scores, generated by principal components analysis, ranged from 1.19 to 5.35. There was little correlation between genetic and morphological distinctiveness across populations, although one population was genetically and morphologically the most distinctive. There was, however, a significant correlation (r = 0.26, P = 0.008) between microsatellite (F ST) and morphological (P ST) divergence. We combined measures of allelic richness, genetic variation within, and divergence among, populations and morphological variation to provide a conservation ranking of populations. Our approach can be combined with other measures of biodiversity value (habitat, rarity, human uses, threat status) to rationalize the prioritization of populations, especially for widespread species where geographic isolation across distinct environments promotes intraspecific variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".